Outlier Detection Module for Audit Trail Data Analysis
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Solution Overview
Problem
Current content management systems lack an efficient method to identify malicious access to documents beyond normal time windows, making manual review impractical due to the vast volume of data and potential for misuse or policy violations.
Innovation Solution
A system with an audit forensics engine and outlier detection module that captures audit trail data, analyzes usage patterns using statistical analysis or predefined rules, and flags deviations as outliers, enabling detection of fraudulent or policy-violating access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of audit trail data is performed, then detection accuracy of policy violations is improved, but productivity deteriorates due to the vast volume of data
Solution Approach 1:
An automated outlier detection module is introduced as an intermediary between the audit trail data and human reviewers. This module automatically analyzes audit trail data using statistical methods and machine learning algorithms to identify outliers that deviate from normal usage patterns, thereby filtering the vast volume of data down to a manageable set of suspicious activities for manual review.
Solution Approach 2:
The system performs self-service by automatically detecting and flagging outliers without requiring manual review of all audit trail data. The outlier detection module autonomously analyzes usage patterns, identifies anomalies, and prioritizes suspicious activities, enabling the system to serve its own detection needs without constant human intervention.
2Productivity
If automated outlier detection is implemented, then productivity is improved by reducing manual review, but device complexity increases due to additional detection modules
Solution Approach 1:
The outlier detection module is designed with multi-functionality, serving multiple purposes: it analyzes audit trail data, identifies outliers using statistical methods, prioritizes suspicious activities, and integrates with existing content management systems. This universal approach allows a single module to handle various detection tasks without requiring separate specialized systems for each function.
Solution Approach 2:
The system uses copying by creating simplified representations of complex audit trail data through usage patterns and statistical models. Instead of manually analyzing raw audit data, the system copies essential features into structured patterns that can be efficiently processed by the outlier detection module, reducing the complexity of direct data analysis.
3Reliability
If comprehensive audit trail monitoring is performed, then reliability of compliance detection is improved, but loss of time increases due to data volume
Solution Approach 1:
The system performs preliminary action by continuously analyzing audit trail data in real-time and pre-identifying outliers before formal compliance reviews are conducted. The outlier detection module maintains up-to-date usage patterns and proactively flags suspicious activities as they occur, enabling early detection without requiring retrospective analysis of entire data sets.
Solution Approach 2:
The audit trail data is segmented into manageable components through the use of usage patterns and statistical models. The outlier detection module divides the comprehensive monitoring task into smaller sub-tasks: pattern establishment, deviation detection, and outlier prioritization. This segmentation allows reliable compliance detection to be performed on segmented data rather than overwhelming volumes of raw data.
Data Source
AI summary
A system and method for detecting an outlier in a usage pattern comprises a computer accessible to perform an operation. The system includes an audit forensics engine having an outlier detection module. When an instance occurs where the operation is performed, audit trail data is captured related to the operation. The outlier detection module determines for the instance where the operation is performed whether the instance is an outlier in a usage pattern based on a comparison of the audit trail data to the usage pattern.